AI-DRIVEN DYNAMIC COVER CROP MODEL FOR SUSTAINABLE AGRICULTURE
DOI:
https://doi.org/10.63503/c.acset.2025.19Keywords:
Sustainable Agriculture, Cover Crops, AI Driven Model, Soil Health, Data Analytic, Barriers to Adoption, Sustainability Impact, Climate AdaptabilityAbstract
The intensive use of chemical fertilizers, pesticides, and mono-cropping systems in modern agriculture has led to significant environmental degradation, soil fertility loss, and decreased biodiversity. Cover crops, which are non-cash crops grown to improve soil health and fertility, offer a promising solution to these challenges. However, their adoption is limited due to various constraints, including economic, knowledge, andmanagement barriers. This study aims to investigate the role of cover crops in sustainable agriculture, identify the barriers to their adoption, and propose strategies for integrating them into different farming systems. Our proposed AI-Driven Adaptive Cover Crop Model (AIDCCM) leverages artificial intelligence, real-time data analytic, and predictive algorithms to optimize cover crop selection, management, and integration into sustainable agriculture. The model workflow involves data collection, analysis, implementation, monitoring, andfeedback loop. The advantages of AIDCCM include precision, scalability, adaptability, farmer- friendliness, and sustainability. We propose a phased implementation strategy, including pilot projects, partnerships, and training programs. AIDCCM has the potential to revolutionize sustainable agriculture by optimizing cover crop integration, addressing ecological, economic, and social challenges, and ensuring a more resilient and sustainable future for global agriculture.
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